Training, Evaluating, and Tuning Deep Neural Network Models with TensorFlow-Slim
This course builds on the training in Marvin Bertin's "Introduction to TensorFlow-Slim", which covered the basic concepts and uses of the TensorFlow-Slim (TF-Slim) API. In a series of lessons designed for learners with basic machine learning knowledge and some previous TensorFlow expe...
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| Idioma: | Inglés |
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Infinite Skills
2017.
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| Edición: | 1st edition |
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| Ver en Biblioteca Universitat Ramon Llull: | https://discovery.url.edu/permalink/34CSUC_URL/1im36ta/alma991009630976306719 |
| Sumario: | This course builds on the training in Marvin Bertin's "Introduction to TensorFlow-Slim", which covered the basic concepts and uses of the TensorFlow-Slim (TF-Slim) API. In a series of lessons designed for learners with basic machine learning knowledge and some previous TensorFlow experience, you'll explore many of TF-Slim's most advanced features; using them to build and train sophisticated deep learning models. As you work through the examples, you'll come to appreciate TF-Slim's primary benefit: Its ability to enable the work of machine learning while avoiding code complexity, a significant problem in the world of increasingly deep neural networks. Learn to construct and customize losses functions for regression, classification, and multi-task problems Discover how to combine various metrics and use them to measure model performance Understand how to automate training and evaluation routines Learn how to train and evaluate a convolutional neural network model See how you can improve model performance by using fine-tuning on pre-trained models Gain experience using transfer learning for new predictive tasks Marvin Bertin is a data scientist with Driver, a San Francisco based biotech startup. Before that, he worked as a deep learning researcher for the AI company Skymind. Marvin holds degrees in Data Science and Mechanical Engineering, has authored a number of courses on deep learning, and is a speaker at machine learning and deep learning conferences. |
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| Notas: | Title from title screen (viewed April 21, 2017). Date of publication from resource description page. |
| Descripción Física: | 1 online resource (1 video file, approximately 1 hr., 13 min.) |
